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Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification - 2018

Deep Learning- And Word Embedding-Based Heterogeneous Classifier Ensembles For Text Classification

Research Paper on Deep Learning- And Word Embedding-Based Heterogeneous Classifier Ensembles For Text Classification

Research Area:  Machine Learning

Abstract:

The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.

Keywords:  
Deep Learning
Word Embedding
Heterogeneous
Classifier
Text Classification
Machine Learning

Author(s) Name:  Zeynep H. Kilimci and Selim Akyokus

Journal name:  Complexity

Conferrence name:  

Publisher name:  Hindawi

DOI:  10.1155/2018/7130146

Volume Information: